Abstract
Spoken language understanding plays an important role in the dialogue systems, and in such systems, intent detection and slot filling tasks are used to extract semantic components. In previous works on spoken language understanding, many ways that from traditional pipeline methods to joint models have been investigated. The features from these methods are usually extracted from one dataset that cannot jointly optimize databases with different distributions. In this paper, we propose a new adversarial shared-private attention network that learns features from two different datasets with shared and private spaces. The proposed adversarial network trains the shared attention network so that the shared distributions of two datasets are close, thereby reducing the redundancy of the shared features, which helps to alleviate the interference from the private and shared space. A joint training strategy between intent detection and slot filling is also applied to enhance the task relationship. Experimental results on public benchmark corpora, called ATIS, Snips and MIT, show that our proposed models significantly outperform other methods on intent accuracy, slot F1 measure and sentence accuracy.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61771333, the Tianjin Municipal Science and Technology Project under Grant 18ZXZNGX00330.
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Wu, M., Wang, L., Si, Y., Dang, J. (2020). Adversarial Shared-Private Attention Network for Joint Slot Filling and Intent Detection. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_71
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